




"""simple docstring"""


import argparse

from torch import nn

# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
    ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
    XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)

from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging


_A						=     logging.get_logger(__name__)
logging.set_verbosity_info()



def 			lowercase_ (					__UpperCAmelCase							,						__UpperCAmelCase )		->							Optional[Any]:
      if "xprophetnet" in prophetnet_checkpoint_path:
            lowerCAmelCase__     :					Tuple       =			XLMProphetNetForConditionalGenerationOld.from_pretrained(__UpperCAmelCase )
            lowerCAmelCase__     :					Any       =			XLMProphetNetForConditionalGeneration.from_pretrained(
                __UpperCAmelCase							,						output_loading_info=__UpperCAmelCase )
      else:
            lowerCAmelCase__     :					List[str]       =			ProphetNetForConditionalGenerationOld.from_pretrained(__UpperCAmelCase )
            lowerCAmelCase__     :					Dict       =			ProphetNetForConditionalGeneration.from_pretrained(
                __UpperCAmelCase							,						output_loading_info=__UpperCAmelCase )

      lowerCAmelCase__     :					List[str]       =			["""key_proj""", """value_proj""", """query_proj"""]

      lowerCAmelCase__     :					Optional[int]       =			{
          """self_attn""": """ngram_self_attn""",
          """cross_attn""": """encoder_attn""",
          """cross_attn_layer_norm""": """encoder_attn_layer_norm""",
          """feed_forward_layer_norm""": """final_layer_norm""",
          """feed_forward""": """""",
          """intermediate""": """fc1""",
          """output""": """fc2""",
          """key_proj""": """k_proj""",
          """query_proj""": """q_proj""",
          """value_proj""": """v_proj""",
          """word_embeddings""": """embed_tokens""",
          """embeddings_layer_norm""": """emb_layer_norm""",
          """relative_pos_embeddings""": """relative_linear""",
          """ngram_embeddings""": """ngram_input_embed""",
          """position_embeddings""": """embed_positions""",
      }

      for key in loading_info["missing_keys"]:
            lowerCAmelCase__     :					Any       =			key.split(""".""" )

            if attributes[0] == "lm_head":
                  lowerCAmelCase__     :					Optional[Any]       =			prophet
                  lowerCAmelCase__     :					str       =			prophet_old
            else:
                  lowerCAmelCase__     :					Union[str, Any]       =			prophet.prophetnet
                  lowerCAmelCase__     :					int       =			prophet_old.model

            lowerCAmelCase__     :					List[Any]       =			False
            for attribute in attributes:
                  if attribute in mapping:
                        lowerCAmelCase__     :					Tuple       =			mapping[attribute]
                        if not hasattr(__UpperCAmelCase							,						__UpperCAmelCase ) and len(__UpperCAmelCase ) > 0:
                              lowerCAmelCase__     :					List[str]       =			attribute
                  elif hasattr(__UpperCAmelCase							,						__UpperCAmelCase ):
                        lowerCAmelCase__     :					List[str]       =			attribute

                  if attribute == "weight":
                        assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
                        lowerCAmelCase__     :					Dict       =			old_model.weight
                        logger.info(f"""{attribute} is initialized.""" )
                        lowerCAmelCase__     :					Tuple       =			True
                        break
                  elif attribute == "bias":
                        assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
                        lowerCAmelCase__     :					int       =			old_model.bias
                        logger.info(f"""{attribute} is initialized""" )
                        lowerCAmelCase__     :					Union[str, Any]       =			True
                        break
                  elif attribute in special_keys and hasattr(__UpperCAmelCase							,						"""in_proj_weight""" ):
                        lowerCAmelCase__     :					Union[str, Any]       =			old_model.in_proj_weight.shape[0] // 3
                        lowerCAmelCase__     :					Optional[Any]       =			getattr(__UpperCAmelCase							,						__UpperCAmelCase )
                        param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
                        param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
                        if attribute == "query_proj":
                              lowerCAmelCase__     :					Optional[int]       =			nn.Parameter(old_model.in_proj_weight[:embed_dim, :] )
                              lowerCAmelCase__     :					List[str]       =			nn.Parameter(old_model.in_proj_bias[:embed_dim] )

                        elif attribute == "key_proj":
                              lowerCAmelCase__     :					List[Any]       =			nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :] )
                              lowerCAmelCase__     :					str       =			nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim] )
                        elif attribute == "value_proj":
                              lowerCAmelCase__     :					Any       =			nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :] )
                              lowerCAmelCase__     :					Optional[Any]       =			nn.Parameter(old_model.in_proj_bias[2 * embed_dim :] )
                        lowerCAmelCase__     :					List[Any]       =			True
                        break
                  elif attribute == "position_embeddings":
                        assert (
                            model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
                        ), "Hidden size has to match"
                        assert model.position_embeddings.weight.shape[0] == 512, "We want 512 position_embeddings."
                        lowerCAmelCase__     :					Any       =			nn.Parameter(old_model.embed_positions.weight[:512, :] )
                        lowerCAmelCase__     :					Union[str, Any]       =			True
                        break

                  if attribute.isdigit():
                        lowerCAmelCase__     :					Any       =			model[int(__UpperCAmelCase )]
                        lowerCAmelCase__     :					Dict       =			old_model[int(__UpperCAmelCase )]
                  else:
                        lowerCAmelCase__     :					Optional[Any]       =			getattr(__UpperCAmelCase							,						__UpperCAmelCase )

                        if old_attribute == "":
                              lowerCAmelCase__     :					Tuple       =			old_model
                        else:
                              if not hasattr(__UpperCAmelCase							,						__UpperCAmelCase ):
                                    raise ValueError(f"""{old_model} does not have {old_attribute}""" )
                              lowerCAmelCase__     :					Union[str, Any]       =			getattr(__UpperCAmelCase							,						__UpperCAmelCase )

            if not is_key_init:
                  raise ValueError(f"""{key} was not correctly initialized!""" )

      print(f"""Saving model to {pytorch_dump_folder_path}""" )
      prophet.save_pretrained(__UpperCAmelCase )


if __name__ == "__main__":
  _A						=     argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--prophetnet_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
  )
  _A						=     parser.parse_args()
  convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)







from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase_    :			Any    =	logging.get_logger(__name__)

lowerCamelCase_    :			Dict    =	{
    """facebook/dpr-ctx_encoder-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-question_encoder-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-reader-single-nq-base""": (
        """https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json"""
    ),
    """facebook/dpr-ctx_encoder-multiset-base""": (
        """https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json"""
    ),
    """facebook/dpr-question_encoder-multiset-base""": (
        """https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json"""
    ),
    """facebook/dpr-reader-multiset-base""": (
        """https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json"""
    ),
}

class 							_UpperCamelCase					(       _A	):






			'''simple docstring'''

			__UpperCamelCase   :    Any          =			"""dpr"""

			def __init__(							self			:							str    ,						snake_case_			:							Any=3_0522    ,						snake_case_			:							List[str]=768    ,						snake_case_			:							List[str]=12    ,						snake_case_			:							Union[str, Any]=12    ,						snake_case_			:							Tuple=3072    ,						snake_case_			:							Any="gelu"    ,						snake_case_			:							Optional[Any]=0.1    ,						snake_case_			:							str=0.1    ,						snake_case_			:							Dict=512    ,						snake_case_			:							int=2    ,						snake_case_			:							Union[str, Any]=0.02    ,						snake_case_			:							str=1e-12    ,						snake_case_			:							List[str]=0    ,						snake_case_			:							Any="absolute"    ,						snake_case_			:							int = 0    ,						**snake_case_			:							int    ,						):
							super().__init__(pad_token_id=snake_case_    ,						**snake_case_   )

							UpperCamelCase_:				str						=    vocab_size
							UpperCamelCase_:				Optional[int]						=    hidden_size
							UpperCamelCase_:				int						=    num_hidden_layers
							UpperCamelCase_:				Any						=    num_attention_heads
							UpperCamelCase_:				int						=    hidden_act
							UpperCamelCase_:				Optional[Any]						=    intermediate_size
							UpperCamelCase_:				Tuple						=    hidden_dropout_prob
							UpperCamelCase_:				Tuple						=    attention_probs_dropout_prob
							UpperCamelCase_:				str						=    max_position_embeddings
							UpperCamelCase_:				Any						=    type_vocab_size
							UpperCamelCase_:				Tuple						=    initializer_range
							UpperCamelCase_:				List[Any]						=    layer_norm_eps
							UpperCamelCase_:				Tuple						=    projection_dim
							UpperCamelCase_:				Dict						=    position_embedding_type






